Instructors:
- Prof. Hung: focusing on mathematical background, equation derivation
- Prof. Chiu: focusing on big picture, key ideas
Textbook: Bishop: Pattern Recognition and Machine Learning
Homework: 7 coding homework, once every other week
Notice: Don’t take this class if you know you won’t have enough time (8-16hrs) or > you won’t spend time on learning.
Description: Write a program for doing regularized linear regression model (polynomail basis) by closed-form LSE approach, Steepest descent and Netwton's method.
Run: python ./HW1/Linear_regression.py
Notice: Hessian Matrix
Description: Create a Naive Bayes classifier for each handwritten digit (MNSIT dataset) that support discrete and continuous.
Run: python ./HW2/Naive_Bayes_Classifier.py
Notice: Distribution of pixel in different mode
Description: Use online learning to the beta distribution of the parameter p of the coin tossing trails in batch.
Run: python ./HW3/Online_Learning.py
Notice: Conjugate prior of beta distribution
Description: Create a generator of Univariate gaussian data and polynomial basis linear model data for the following two task.
Run: python ./HW4/Rand_datagen.py
Notice: Approximate normal distribution in computor
Description: Sequential estimate the mean and variance of the squentail data generate from N(m, s)
Run: python ./HW4/Sequntial_estimator.py
Notice: Converage condition (assume we don't know the actual mean and variance)
Description: Complete the baysian linear regression and visualize the process.
Run: python ./HW4/Bayesian_Linear_Regression.py
Notice: Different gaussian distribution type of prior, likelihood, posterior, predictive
Description: Generate two dataset D1, D2 and seperate them using Newton's method and steepest gradient. Visualize the results then show the confusion matrix and sensitivity and specificity.
Run: python ./HW4/Logistic_regression.py
Notice: the meaning of weights and what does it help for logistic regression
Description: Build an unsupervised learning model using EM algorithm for MNIST dataset classification. Show confusion matrix and sensitivity and specificity.
Run: python ./HW4/EM_algorithm.py
Notice: Cluster mapping